Biological Systems Engineering


First Advisor

Joe D. Luck

Date of this Version



A THESIS Presented the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Mechanized Systems Management, Under the Supervision of Professor Joe D. Luck. Lincoln, Nebraska: May 2021

Copyright © 2021 Samantha L. Teten


Optimizing nitrogen (N) fertilizer applications in corn to reduce environmental impacts while maintaining producer profitability remains a challenge due to spatial and temporal variability in crop yield potential and soil N dynamics. In response to these challenges, active crop canopy sensors and imagery systems have been studied to test the performance of vegetative index-based N management, but adoption has been low. There is also a lack of field-scale research evaluating this technology in water-limiting environments.

The evaluation of two sensor-based N management techniques was completed at nine non-irrigated sites in Eastern Nebraska. The first sensor-based N management technique evaluated an active crop canopy sensor and Holland-Schepers model to direct real-time, in-season N applications on corn. Compared to growers’ management, active sensor management improved N use efficiency (NUE) by 16.8±8.4 kg grain kg N-1 and reduced N fertilizer inputs by 38.7±20.8 kg N ha-1. All sites resulted in less N applied than the growers’ management. Two of the nine sites resulted in significant yield losses compared to the sensor-based management with an average yield loss across all sites of 0.49±0.69 Mg grain ha-1. Average partial profitability was $2.40±15.48 US$ ha-1 less than the growers’ practices. Early season base N rates and timing influenced the NUE of active sensor N management approach.

The second sensor-based management technique utilized aerial imagery and the Holland-Schepers model to develop variable-rate N prescriptions for in-season applications. The approach incorporated sub-field yield potential by varying the estimated optimum N rate used in the algorithm based on management zones (MZ). The aerial imagery-based management improved NUE compared to the growers’ current management by 23.6±15.3 kg grain kg N-1 and did not result in differences in partial profit. The integration of MZs influenced the total N applied and demonstrated the potential to improve imagery-based recommendations using spatial field data.

Overall, compared to grower management, active sensors improved NUE in non-irrigated sites where rainfall is a yield limiting factor. Aerial imagery-based prescriptions also improved NUE compared to grower management and provided an opportunity to further refine sensor-based management to account for sub-field variability by incorporating yield potential and soil attributes.

Advisor: Joe D. Luck